Atomistic Surrogate-Based Optimization for Simulation-Driven Design of Computationally Expensive Microwave Circuits with Compact Footprints

2014 ◽  
pp. 195-218 ◽  
Author(s):  
Piotr Kurgan ◽  
Adrian Bekasiewicz
Keyword(s):  
Microwave Circuits ◽  
Surrogate Based Optimization ◽  
Computationally Expensive

scholarly journals A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

2020 ◽  
Vol 62 (4) ◽  
pp. 1787-1807
Author(s):  
Jin Yi ◽  
Yichi Shen ◽  
Christine A. Shoemaker
Keyword(s):  
Capacity Planning ◽  
Manufacturing Systems ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Discrete Event ◽  
Optimal Solution ◽  
Planning Problem ◽  
Optimization Framework ◽  
Surrogate Based Optimization ◽  
Computationally Expensive

Abstract This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynamic coordinate search algorithm using response surface) algorithm to search on the LF model and then employ a potential area detection procedure to identify the promising points from the LF model. The promising points serve as the initial start points when we further search for the optimal solution based on the HF model. The performance of MRSO is compared with 6 other surrogate-based optimization methods (4 are using a single-fidelity surrogate and the rest 2 are using multi-fidelity surrogates). The comparisons are conducted on a multi-fidelity optimization test suite containing 10 problems with 10 and 30 dimensions. Besides the benchmark functions, we also apply the proposed algorithm to a practical and computationally expensive capacity planning problem in manufacturing systems which involves discrete event simulations. The experimental results demonstrate that MRSO outperforms all the compared methods.

On strategy for determining displacement solely from perturbed phase of self mixing interferometry

2020 ◽  
Author(s):  
Junaid Khan
Keyword(s):  
Coupling Factor ◽  
Moving Target ◽  
Linear Phase ◽  
Spectral Filtering ◽  
Signal Shape ◽  
Phase Equation ◽  
Sensing Applications ◽  
Computationally Expensive ◽  
Fixed Point Methods ◽  
Factor C

While self mixing interferometry(SMI) has proven to be suitable for displacement measurement and other sensing applications,its characteristic self mixing signal shape is strongly governed by the non-linear phase equation which forms relation between perturbed and unperturbed phase of self mixing laser.Therefore, while it is desirable for robust estimation of displacement of moving target, the algorithms to achieve this must have an objective strategy which can be achieved by understanding the characteristic of extracting knowledge of perturbed phase from unperturbed phase. Therefore, it has been proved and shown that such strategy must not involve sole methods where perturbed phase is continuous function of unperturbed phase (e.g:Taylor series or fixed point methods) or through successive displacements (e.g: variations of Gauss Seidal method). Subset of this strategy is to perform spectral filtering of perturbed phase followed by perturbative or homotopic deformation. A less computationally expensive approach of this strategy is adopted to achieve displacement with mean error of 62.2nm covering all feedback regimes, when coupling factor 'C' is unknown.<br>

scholarly journals Eco-Driving for Different Electric Powertrain Topologies Considering Motor Efficiency

2021 ◽  
Vol 12 (1) ◽  
pp. 6
Author(s):  
Alexander Koch ◽  
Tim Bürchner ◽  
Thomas Herrmann ◽  
Markus Lienkamp
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicles ◽  
Quadratic Functions ◽  
Simultaneous Optimization ◽  
Speed Profile ◽  
Power Demand ◽  
Electric Motors ◽  
Car Following ◽  
Motor Efficiency ◽  
Computationally Expensive

Electrification and automatization may change the environmental impact of vehicles. Current eco-driving approaches for electric vehicles fit the electric power of the motor by quadratic functions and are limited to powertrains with one motor and single-speed transmission or use computationally expensive algorithms. This paper proposes an online nonlinear algorithm, which handles the non-convex power demand of electric motors. Therefore, this algorithm allows the simultaneous optimization of speed profile and powertrain operation for electric vehicles with multiple motors and multiple gears. We compare different powertrain topologies in a free-flow scenario and a car-following scenario. Dynamic Programming validates the proposed algorithm. Optimal speed profiles alter for different powertrain topologies. Powertrains with multiple gears and motors require less energy during eco-driving. Furthermore, the powertrain-dependent correlations between jerk restriction and energy consumption are shown.

Efficient downscaling of satellite oceanographic data with convolutional neural networks

2021 ◽  
Vol 12 (3) ◽  
pp. 46-47
Author(s):  
Nikita Saxena
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Sea Surface ◽  
Clear Sky ◽  
Oceanographic Data ◽  
Computationally Expensive ◽  
Sky Conditions ◽  
Data Inference

Space-borne satellite radiometers measure Sea Surface Temperature (SST), which is pivotal to studies of air-sea interactions and ocean features. Under clear sky conditions, high resolution measurements are obtainable. But under cloudy conditions, data analysis is constrained to the available low resolution measurements. We assess the efficiency of Deep Learning (DL) architectures, particularly Convolutional Neural Networks (CNN) to downscale oceanographic data from low spatial resolution (SR) to high SR. With a focus on SST Fields of Bay of Bengal, this study proves that Very Deep Super Resolution CNN can successfully reconstruct SST observations from 15 km SR to 5km SR, and 5km SR to 1km SR. This outcome calls attention to the significance of DL models explicitly trained for the reconstruction of high SR SST fields by using low SR data. Inference on DL models can act as a substitute to the existing computationally expensive downscaling technique: Dynamical Downsampling. The complete code is available on this Github Repository.

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